Distributed Artificial Intelligence for IoT Security: A Structured Review
TL;DR
This paper presents a structured review of Distributed Artificial Intelligence (DAI) for IoT security, focusing on how local, cooperative intelligence can support intrusion detection, anomaly recognition, secure data processing, and collaborative defence.
Abstract
The expansion of the Internet of Things (IoT) has increased the complexity of securing distributed systems against growing threats to data security, privacy, and reliability. Conventional centralised cybersecurity methods are often insufficient for environments characterised by scale, heterogeneity, and dynamic behaviour. This paper presents a structured review of Distributed Artificial Intelligence (DAI) for IoT security, focusing on how local, cooperative intelligence can support intrusion detection, anomaly recognition, secure data processing, and collaborative defence. We synthesise the current literature on Federated Learning (FL), Multi-Agent Systems, and related approaches, highlighting their benefits, limitations, and practical deployment constraints. Particular attention is given to critical infrastructure contexts, where resilience is essential for operational continuity and public safety. The review concludes by outlining key gaps and future research directions for DAI-enabled IoT security.